Innovation based in data analytics is a contemporary approach to developing empirically supported advances that encourage entrepreneurial activity inspired by novel marketing inferences. Data Analytics in Marketing, Entrepreneurship, and Innovation covers techniques, processes, models, tools, and practices for creating business opportunities through data analytics. It features case studies that provide realistic examples of applications. This multifaceted examination of data analytics looks at: Business analytics Applying predictive analytics Using discrete choice analysis for decision-making Marketing and customer analytics Developing new products Technopreneurship Disruptive versus incremental innovation The book gives researchers and practitioners insight into how data analytics is used in the areas of innovation, entrepreneurship, and marketing. Innovation analytics helps identify opportunities to develop new products and services, and improve existing methods of product manufacturing and service delivery. Entrepreneurial analytics facilitates the transformation of innovative ideas into strategy and helps entrepreneurs make critical decisions based on data-driven techniques. Marketing analytics is used in collecting, managing, assessing, and analyzing marketing data to predict trends, investigate customer preferences, and launch campaigns.

Since the beginning of time, running a business has involved using logic by which the business operates. This logic is called the business model in management science, which increasingly is focusing on issues surrounding business models. Research trends related to business models include value creation, value chain operationalization, and social and ecological aspects, as well as innovation and digital transformation. Business Models: Innovation, Digital Transformation, and Analytics examines how innovation, digital transformation, and the composition of value affect the existence and development of business models. The book starts by addressing the conceptual development of business models and by discussing the essence of innovation in those models. Chapters in the book investigate how: Business models can analyze digital transformation scenarios Individual business model elements effect selected performance measures as well as how the elements are significant for the enterprise value composition The environment effects the profitability of the high-growth enterprise business models Employer branding business models are perceived by the generation Z workforce To implement responsible business models in the enterprise Cyber risk is captured in business models Decision algorithms are important to business analytics This book is a compendium of knowledge about the use of business models in the context of innovative activities, digital transformation, and value composition. It attempts to combine the theory and practice and offers a look at business models currently used in companies, especially high-growth enterprises, in various countries of the world and indicates the prospects for their development.

The digital age has transformed business opportunities and strategies in a resolutely practical and data-driven project universe. This book is a comprehensive and analytical source on entrepreneurship and Big Data that prospective entrepreneurs must know before embarking upon an entrepreneurial journey in this present age of digital transformation. This book provides an overview of the various aspects of entrepreneurship, function, and contemporary forms. It covers a real-world understanding of how the entrepreneurial world works and the required new analytics thinking and computational skills. It also encompasses the essential elements needed when starting an entrepreneurial journey and offers inspirational case studies from key industry leaders. Ideal reading for aspiring entrepreneurs, Entrepreneurship and Big Data: The Digital Revolution is also useful to students, academicians, researchers, and practitioners.

This innovative book proposes new methodologies for the measurement of entrepreneurship by applying techniques of demography, engineering, mathematics and statistics. Using the data from the Global Entrepreneurship Monitor (GEM), statistical demographic techniques are used for the evaluation of data quality (EDQ), and a new methodology for the estimation of Specific Entrepreneurship Rates (SER) and the Global Entrepreneurship Rate (GER) is proposed. At the same time the authors present artificial intelligence techniques such as Fuzzy Time Series (FTS) to forecast data series of the entrepreneurial population. Finally, they present a case study of the implementation of Big Data in Entrepreneurship using GEM data that shows the latest technological trends for the management of data, in support of making more accurate decisions. Being a methodological book, the techniques presented can be applied to any dataset in different areas. Readers will learn new methodologies of analysis and measurement of entrepreneurship using data from the Global Entrepreneurship Monitor. They will be able to access the experience of the authors through each of the applied cases in which the reader is taken by the hand, both through the scientific method and through the methodology of construction of more accurate metrics in entrepreneurship, with less error. This book will be of value to students at an advanced level, academics and researchers in the fields of Entrepreneurship, Business Analytics and Research Methodology.

Media Innovation & Entrepreneurship is an open, collaboratively written and edited volume designed to fill the needs of a growing number of journalism and mass communications programs in the U.S. that are teaching media entrepreneurship, media innovation, and the business of journalism to undergraduate and graduate students.

This book offers a unique view of how innovation and competitiveness improve when organizations establish alliances with partners who have strong capabilities and broad social capital, allowing them to create value and growth as well as technological knowledge and legitimacy through new knowledge resources. Organizational intelligence integrates the technology variable into production and business systems, establishing a basis to advance decision-making processes. When strategically integrated, these factors have the power to promote enterprise resilience, robustness, and sustainability. This book provides a unique perspective on how knowledge, information, and data analytics create opportunities and challenges for sustainable enterprise excellence. It also shows how the value of digital technology at both personal and industrial levels leads to new opportunities for creating experiences, processes, and organizational forms that fundamentally reshape organizations.

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.